# Bayes Theorem – Statistics Part 20

Hey Developer’s, I’m back with a new topic which is Bayes Theorem in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Bayes Theorem in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Law of Total Probability in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Multiplicative and Additive Law Of Probability in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Independence Of Events in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Conditional and Unconditional Probability in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Probability of Union Of Events in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Multinomial Coefficients in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Binomial Coefficients in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Combinatorics in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Permutations in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Counting Sample Points in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Discrete and Continuous Probability in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Rules of Probability in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Introduction To Probability and Examples in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Set Operations On Events in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Experiments, Events, Sample Spaces & Points in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is Cardinality, Set Complement and Set Laws in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is a Sets Membership in the series of statistics foundations.

Hey Developer’s, I’m back with a new topic which is an introduction to sets in the series of statistics foundations.

Hey Developer, I’m back with a new series of blogposts on the foundation of statistics for data science and machine learning. Here will be covering all the topics that are essential for good understanding of data science and machine learning concepts.

Decision Tree is a type of Supervised Learning Algorithm wherein the data is continuously split on the basis of certain parameters. To understand the decision tree in a better way let’s take an example

Google has inked a deal with India’s third-largest telecom operator as the American giant looks to grow its cloud customer base in the key overseas market that is increasingly emerging as a new cloud battleground for AWS and Microsoft .

Machine learning resources containing Deep Learning, Machine Learning and Artificial Intelligent resources. A-Z Machine learning resources to learn machine learning.

Affine Transformation helps to modify the geometric structure of the image, preserving parallelism of lines but not the lengths and angles. It preserves collinearity and ratios of distances. It is one type of method we can use in Machine Learning and Deep Learning for Image Processing and also for Image Augmentation.

AI, ML and DL are related to each other. AI is a superset of ML and DL. What we do in the field of ML and DL all comes under AI. To better understand all of them, Let’s dive in…

A hyperparameter is a parameter or a variable we need to set before applying a machine learning algorithm into a dataset.These parameters express “High Level” properties of the model such as its complexity or how fast it should learn. Hyperparameters are usually fixed before the actual training process begins.

In this notebook we will be learning how to use Transfer Learning to create the powerful convolutional neural network with a very little effort, with the help of MobileNetV2 developed by Google that has been trained on large dataset of images.

Training error should steadily decrease, steeply at first, and should eventually plateau as training converges.If the training has not converged, try running it for longer.

In the real world, it is very difficult to explain behavior as a function of only one variable, and economics is no different.

Deep Learning is a subfield of Machine Learning because it makes use of Deep Neural Networks inspired by the structure and function of the brain called Artificial Neural Networks.

Regression is basically a statistical approach of finding a relationship between the variables. Linear regression is one type of regression we use in Machine Learning.

Here are 15 Best Machine Learning Course for Machine Learning. It will give you the great knowledge about Machine Learning and Deep Learning.

We all love to see beautiful images, but have you ever thought how do computers see an image? In this tutorial, we will give an explanation of how images are stored in a computer.

CNN’s achieve state of the art results in the variety of problem areas including Voice User Interfaces, Natural Language Processing, and Computer Vision.

Though there are various fields out there which requires a laptop with good specifications and you can get it at an affordable price but that’s not the same case for deep learning.

Machine Learning today is one of the most sought-after skills in the market. Here are some of the best books which you can use to learn Machine Learning.

The NumPy library is the core library for scientific computing in Python. It provides a high-performance multidimensional array object, and tools for working with these arrays.

Foundation is the basement for a healthy home. So here comes with the languages too which acts…

Before, to train an AI model that can recognize whatever you want it to recognize in pictures, involves lots of expertise in Applied Mathematics and use Deep Learning Libraries. To write the code for the algorithm and fit the code to your images involves lots of time and stress.

Logic as well as discrete mathematics are premise for computer based disciplines such as Computer Science, Software engineering and Information Practices.

Java is a general-purpose programming language that is class-based, object-oriented, and specifically designed to have as few implementation dependencies as possible

When you work as a developer everything seems challenging at the beginning whether it is the Functionality, or Storing the precious data that our user is using.

The art and science of :

Giving Computers the ability to learn,

To make decisions from data,

Without being explicitly programmed .

Let’s Encrypt is a free, automated, and open certificate authority brought to you by the non-profit Internet Security Research Group (ISRG). It gives you free Certificates for your website. You can also get free SSL certificate from this website.

Here goes the learning path to become an expert in machine learning.Learn any programming language (Python is highly preferable)

Introduction to Tensorflow the core open source library to help you develop and train ML models.

Here, Github gives us the opportunity to use this software for free in its Github Student Developer Pack. So, that you ship software like a pro. Github collaborated with many organizations and made this software available you for free.

We will be downloading Python form its official website which is listed below and then installing it in the windows operating system. Follow the below step for the successful set up of Python.